title: Human neuroimaging as a "Big Data" science.
abstract: The maturation of in vivo neuroimaging has led to incredible quantities of digital information about the human brain. While much is made of the data deluge in science, neuroimaging represents the leading edge of this onslaught of "big data". A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Yet few, if any, common solutions exist to support the science of neuroimaging. In this article, we discuss how modern neuroimaging research represents a multifactorial and broad ranging data challenge, involving the growing size of the data being acquired; sociological and logistical sharing issues; infrastructural challenges for multi-site, multi-datatype archiving; and the means by which to explore and mine these data. As neuroimaging advances further, e.g. aging, genetics, and age-related disease, new vision is needed to manage and process this information while marshalling of these resources into novel results. Thus, "big data" can become "big" brain science.
authors.affiliation: The Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, 2001 North Soto Street-Room 102, MC 9232, Los Angeles, CA, 90089-9235, USA, jvanhorn@usc.edu.
authors.forename: John Darrell
authors.initials: JD
authors.lastname: Van Horn
authors.forename: Arthur W
authors.initials: AW
authors.lastname: Toga
mesh_terms: D019295:Computational Biology / Q000379:methods / Q000592:standards; D057225:Data Mining / Q000379:methods / Q000592:standards; D016208:Databases, Factual / Q000592:standards; D023281:Genomics / Q000379:methods / Q000592:standards; D006801:Humans; D033181:Information Dissemination / Q000379:methods*; D015337:Multicenter Studies as Topic / Q000379:methods / Q000592:standards; D059906:Neuroimaging / Q000379:methods* / Q000592:standards